Network analysis, chemical characterization, antioxidant and enzyme inhibitory effects of foxglove (Digitalis cariensis Boiss. ex Jaub. & Spach): A novel raw material for pharmaceutical applications

The present study outlines the phenolic composition and pharmacological properties of different extracts of Digitalis cariensis Boiss. ex Jaub. & Spach root and aerial parts. The metabolic profiles of the studied extracts were characterized by UHPLC-MS. The in vitro antioxidant and enzyme (acetylcholinesterase (AChE), butyrylcholinesterase (BChE), tyrosinase, α-amylase, and α-glucosidase) inhibitory potential of the extracts were determined. Bioinformatics and docking investigations were also conducted to support the enzyme inhibition test and predict putative targets for potential pharmacological applications. Overall, the methanolic extract followed by the water extract of the D. cariensis root were found to be superior source of antioxidant compounds except for metal chelating ability, in which the water extract of the root (26.34 ± 1.54 mg EDTAE/g) and aerial parts (16.47 ± 0.88 mg EDTAE/g) have showed the highest activity. The tested extracts were potent against AChE (9.11 ± 0.27-9.79 ± 0.28 mg GEs/g extract), α-amylase (0.12 ± 0.01- 0.50 ± 0.01 mmol ACEs/g extract) and α-glucosidase (0.28 ± 0.01-17.29 ± 0.24 mmol ACEs/ g extract). Notable activity against tyrosinase was displayed by the methanolic extracts (Root-MeOH: 123.71 ± 2.70 and aerial parts - MeOH: 137.96 ± 1.07 mg KAE/g extract), while none of the extracts were potent against BChE. According to docking investigations, the observed anti-tyrosinase effect could be related, at least partially, to the presence of luteolin, rosmarinic acid and kaempferol in the extracts. Results amassed herein is the first report on the biological attributes of D. cariensis, which validate the pharmacological uses of this plant.

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform.